🌐 Distributed Application Scenario

"Order Processing Pipeline with Queue Backed Microservices"

A retail platform processes customer orders using a distributed, event-driven architecture:

1. Frontend Web App → sends order to
2. API Gateway → forwards to
3. Order Service → writes order to
4. Message Queue (e.g., Azure Queue, SQS, RabbitMQ) → consumed by
5. Payment Service → calls
6. External Payment Provider → returns result to
7. Order Service → updates
8. Database → emits
9. Order Events → consumed by
10. Notification Service (email/SMS)
11. Analytics Service
12. Inventory Service

Each component is independently deployed, scaled, and versioned.

💥 All Possible Failure Points

Organized by layer so you can drop this directly into a lecture or architecture review.

🧩 1. Frontend → API Gateway

• Network latency or packet loss
• TLS handshake failures
• DNS resolution failures
• API Gateway throttling
• CORS misconfiguration
• Stale client-side caches
• Browser timeouts

🚪 2. API Gateway → Order Service

• Gateway routing misconfiguration
• JWT token validation failures
• Rate limiting or WAF blocking
• Order Service instance unavailable
• Order Service cold start delays
• Serialization/deserialization errors
• Request body too large
• Timeout between gateway and service

📦 3. Order Service → Database

• Connection pool exhaustion
• Deadlocks
• Slow queries causing cascading timeouts
• Transaction conflicts
• Schema drift between services
• Database failover events
• Partial writes (e.g., order saved but audit log not written)
• Lost writes due to optimistic concurrency failures

📬 4. Order Service → Message Queue

• Queue unavailable
• Queue throttling
• Message size too large
• Duplicate message enqueue due to retry logic
• Message not acknowledged due to timeout
• Poison messages causing repeated retries
• Incorrect visibility timeout leading to double processing
• Queue depth spikes causing backpressure

⚙️ 5. Message Queue → Payment Service

• Consumer crashes mid-processing
• Consumer takes too long → message becomes visible again
• Consumer scaling too aggressively → thundering herd
• Consumer scaling too slowly → backlog grows
• Message lock renewal failures
• Incorrect retry policy (too aggressive or too lenient)
• Messages delivered out of order
• Consumer reading from wrong queue or wrong version of schema

💳 6. Payment Service → External Payment Provider

This is where the nastiest failures happen.

• Provider downtime
• Provider rate limits
• Provider API version mismatch
• Network timeouts
• Partial failures (charge succeeded but response lost)
• Idempotency key not used → duplicate charges
• Provider returns 200 OK but with error payload
• Provider returns 500 but charge actually succeeded
• Provider latency spikes → cascading timeouts
• TLS certificate rotation issues

🔁 7. Retry Logic & Timeouts

This is where distributed systems get dangerous.

Timeouts

• Too short → false failures
• Too long → thread starvation
• Mismatched timeouts between layers → retry storms
• Timeout + retry → duplicate side effects

Retries

• Exponential backoff misconfigured
• No jitter → synchronized retry waves
• Retrying non-idempotent operations
• Retrying after downstream is already saturated

🧨 8. "Releasing Too Many Queue Transactions After Downtime"

This is a classic failure mode.

Scenario

Payment Service is down for 30 minutes. Queue accumulates 50,000 messages. Service comes back online and auto-scales to 50 instances.

Failure Points

• Thundering herd: all instances pull messages at once
• Downstream provider overwhelmed → more failures → more retries
• Visibility timeouts expire → messages reappear → double processing
• Duplicate charges if idempotency not enforced
• Database overwhelmed by update events
• Inventory Service receives thousands of events at once
• Notification Service sends duplicate emails
• Analytics Service receives out-of-order events
• Queue auto-scaling triggers cost explosion
• Circuit breakers open → cascading failures across microservices

This is exactly why distributed systems need:

• backpressure
• rate limiting
• circuit breakers
• idempotency
• dead letter queues
• message deduplication
• controlled drain modes after downtime

🧵 9. Event Bus → Downstream Services

• Event schema mismatch
• Event versioning issues
• Event ordering lost
• Duplicate events
• Event bus outage
• Consumer lag
• Consumer crashes
• Event replay causing duplicate side effects
• Eventual consistency delays confusing users

📣 10. Notification Service

• Email/SMS provider downtime
• Provider throttling
• Duplicate notifications
• Wrong template version
• Notification sent before payment confirmed
• Notification lost due to transient network failure

📊 11. Analytics Service

• Event ingestion lag
• Out-of-order events
• Duplicate events
• Schema mismatch
• Data lake write failures
• Batch job failures
• Partial ingestion leading to incorrect dashboards

📦 12. Inventory Service

• Race conditions updating stock
• Double decrements due to duplicate messages
• Stock not updated due to consumer crash
• Event replay causing negative inventory
• Database deadlocks
• Cache invalidation failures

🧰 13. Infrastructure & Platform Failures

• Container orchestrator (Kubernetes) node failures
• Pod evictions
• Misconfigured liveness/readiness probes
• Autoscaler misfires
• Network partition between services
• DNS propagation delays
• Certificate expiration
• Cloud region outage
• Misconfigured secrets or key rotation

🧠 Summary Table